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DATALAB

  • Center for Digital Social Research

About DATALAB


DATALAB – Center for Digital Social Research is an interdisciplinary research center established in 2016 under the School of Communication and Culture at Aarhus University, Denmark. Led by Professor Anja Bechmann, the center conducts forefront research on algorithmic communication platforms and citizens, collectives, and populations in datafied societies. The center focuses on AI-powered platforms, associated socio-technical actors, patterns of agency and influence, and effects on communication flows. 

DATALAB hosts fundamental research projects that are theoretically based, empirically tested, and often including large-scale trace data. Our research has also contributed to informing decisions on policy and regulatory frameworks (e.g. in relation to platforms and AI). The projects at the center utilize a wide range of methods from computational social science often combining learning models with experiments, surveys, digital ethnography, and interviews.   

DATALAB researchers and projects share a vision and fundamental interest in creating novel methods and reinterpreting theories to better understand platforms and the modern techno-social fabric. Our projects provide novel knowledge on algorithmic and data-driven agency and societies with a particular sensitivity towards principles of democracy, human rights, and ethics.


Contact


Anja Bechmann

Center Director
anjabechmann@cc.au.dk
+45 5133 5138





Recent Publications


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Trecca, F., Tylén, K., Fusaroli, R., Johansson, C. & Christiansen, M. H. (2019). Top-down information is more important in noisy situations: Exploring the role of pragmatic, semantic, and syntactic information in language processing. In A. K. Goel, C. M. Seifert & C. Freksa (Eds.), Proceedings of the the 41st Annual Meeting of the Cognitive Science Society (pp. 2988-2994). Cognitive Science Society. https://doi.org/10.31234/osf.io/xp736
Tran, H. V., Zhang, Z., Pham, T. D., Doan, N. P., Hoang, A.-T., Li, P., Vandierendonck, H., Assent, I. & Mai, T. S. (2025). InteDisUX: intepretation-guided discriminative user-centric explanation for time series. Proceedings of the AAAI Conference on Artificial Intelligence, 39(20), 20921-20928. https://doi.org/10.1609/aaai.v39i20.35387
Tommasel, A., Pablos Sarabia, R. & Assent, I. (2023). Re2Dan: Retrieval of Medical Documents for e-Health in Danish. In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023 (pp. 1208-1211). Association for Computing Machinery. https://doi.org/10.1145/3604915.3610655
Tommasel, A. & Assent, I. (2023). Recommendation fairness and where to find it: An empirical study on fairness of user recommender systems. In J. He, T. Palpanas, X. Hu, A. Cuzzocrea, D. Dou, D. Slezak, W. Wang, A. Gruca, J. C.-W. Lin & R. Agrawal (Eds.), IEEE International Conference on Big Data, BigData 2023, Sorrento, Italy, December 15-18, 2023 (pp. 4195-4204). IEEE. https://doi.org/10.1109/BIGDATA59044.2023.10386616
Tommasel, A. & Assent, I. (2024). Semantic grounding of LLMs using knowledge graphs for query reformulation in medical information retrieval. In W. Ding, C.-T. Lu, F. Wang, L. Di, K. Wu, J. Huan, R. Nambiar, J. Li, F. Ilievski, R. Baeza-Yates & X. Hu (Eds.), 2024 IEEE International Conference on Big Data (BigData) (pp. 4048-4057). IEEE. https://doi.org/10.1109/BigData62323.2024.10826117, https://doi.org/10.1109/BigData62323.2024.10826117

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